Wind Power Generation Scheduling via Historical Data Comparison
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Solution Overview
Problem
Intermittent and uncontrollable electric power generators, such as wind-powered generators, face challenges in providing accurate and up-to-date estimates of electricity production, leading to economic and compliance penalties due to variations between scheduled and actual output.
Innovation Solution
A method that utilizes past electric power production data to provide up-to-date estimates by comparing current production to historical data, adjusting the schedule, and reporting the adjusted estimate to the grid controller to minimize discrepancies between scheduled and actual power generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If intermittent generators are integrated into the power grid to displace less economical generation sources, then environmental and economic benefits are improved, but accurate estimation and scheduling of power production becomes more difficult
Solution Approach 1:
The system performs preliminary actions by obtaining and analyzing historical production data and weather forecasts before the actual power generation period. This allows the administrator to create an initial power production estimate in advance, which is then continuously refined by comparing actual production against predictions, enabling proactive scheduling adjustments rather than reactive corrections
Solution Approach 2:
The system implements continuous feedback loops where actual power production data is constantly compared against estimated production. When deviations are detected, the system automatically adjusts future estimates and generates schedule revisions. This feedback mechanism enables the system to learn from past performance and improve estimation accuracy over time, directly addressing the measurement precision challenge
2Reliability
If detailed and frequent schedule updates are provided to the grid controller, then reliability of power delivery is improved, but administrative complexity and computational requirements increase
Solution Approach 1:
The system employs dynamic adjustment of scheduling estimates based on real-time conditions. Rather than using static schedules, the system continuously adapts estimates by comparing actual production against predictions and automatically generates revised schedules when deviations occur. This dynamic approach maintains high reliability while avoiding unnecessary administrative overhead by only updating schedules when actually needed
Solution Approach 2:
The system changes key parameters such as the weighting factors in estimation algorithms and the thresholds for triggering schedule revisions based on observed performance patterns. By adjusting these parameters dynamically, the system optimizes the balance between update frequency and administrative burden, providing sufficient detail to grid controllers without overwhelming complexity
3Loss of energy
If economic and compliance penalties are minimized through accurate scheduling, then financial performance is improved, but the need for continuous monitoring and adjustment increases operational burden
Solution Approach 1:
The system performs self-service by automatically obtaining historical data, analyzing weather forecasts, comparing actual production against estimates, and generating schedule revisions without requiring constant manual intervention. The automated comparison and adjustment mechanisms reduce the time and resources needed for monitoring while maintaining accurate scheduling to minimize financial penalties
Solution Approach 2:
The system replaces manual mechanical processes of schedule preparation and adjustment with automated computational methods. Computer algorithms perform the data analysis, estimation, and schedule generation that would otherwise require significant human time and effort, thereby reducing operational burden while maintaining or improving scheduling accuracy
Data Source
AI summary
A method for administering an intermittent source of electrical power generation, such as a wind powered electric power generating facility, to produce electric power at a level up to a pre-set level during a selected time period.